In the past, traditional fusion methods prevailed, including [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}. Recently, deep learning-based methods have shown great potential in numerous image-processing fields [9]}, [10]}, [11]} by their powerful nonlinear feature extraction capabilities. Learning-based IVIF approaches achieve promising performance based on the design of various fusion rules and loss functions. We can roughly divide current learning schemes into two categories: fusion-rule based methods [12]}, [13]}, [14]}, [15]} and end-to-end learning schemes [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}.
